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Descriptions

Snags (standing dead trees) are an essential structural component of forests. Because wildlife use of snags
depends on size and decay stage, snag density estimation without any information about snag quality
attributes is of little value for wildlife management decision makers. Little work has been done to develop
models that allow multivariate estimation of snag density by snag quality class. Using climate, topography,
Landsat TM data, stand age and forest type collected for 2356 forested Forest Inventory and Analysis
plots in western Washington and western Oregon, we evaluated two multivariate techniques for their
abilities to estimate density of snags by three decay classes. The density of live trees and snags in three
decay classes (D1: recently dead, little decay; D2: decay, without top, some branches and bark missing;
D3: extensive decay, missing bark and most branches) with diameter at breast height (DBH)P12.7 cm
was estimated using a nonparametric random forest nearest neighbor imputation technique (RF) and a
parametric two-stage model (QPORD), for which the number of trees per hectare was estimated with a
Quasipoisson model in the first stage and the probability of belonging to a tree status class (live, D1,
D2, D3) was estimated with an ordinal regression model in the second stage. The presence of large snags
with DBHP50 cm was predicted using a logistic regression and RF imputation. Because of the more
homogenous conditions on private forest lands, snag density by decay class was predicted with higher
accuracies on private forest lands than on public lands, while presence of large snags was more accurately
predicted on public lands, owing to the higher prevalence of large snags on public lands. RF outperformed
the QPORD model in terms of percent accurate predictions, while QPORD provided smaller root mean
square errors in predicting snag density by decay class. The logistic regression model achieved more
accurate presence/absence classification of large snags than the RF imputation approach. Adjusting the
decision threshold to account for unequal size for presence and absence classes is more straightforward
for the logistic regression than for the RF imputation approach. Overall, model accuracies were poor in
this study, which can be attributed to the poor predictive quality of the explanatory variables and the
large range of forest types and geographic conditions observed in the data.